There is a strong incentive to develop versatile learning techniques that can
transfer the knowledge of class-separability from a labeled source domain to an
unlabeled target domain in the presence of a domain-shift. Existing domain
adaptation (DA) approaches are not equipped for practical DA scenarios as a
result of their reliance on the knowledge of source-target label-set
relationship (e.g. Closed-set, Open-set or Partial DA). Furthermore, almost all
prior unsupervised DA works require coexistence of source and target samples
even during deployment, making them unsuitable for real-time adaptation. Devoid
of such impractical assumptions, we propose a novel two-stage learning process.
1) In the Procurement stage, we aim to equip the model for future source-free
deployment, assuming no prior knowledge of the upcoming category-gap and
domain-shift. To achieve this, we enhance the model's ability to reject
out-of-source distribution samples by leveraging the available source data, in
a novel generative classifier framework. 2) In the Deployment stage, the goal
is to design a unified adaptation algorithm capable of operating across a wide
range of category-gaps, with no access to the previously seen source samples.
To this end, in contrast to the usage of complex adversarial training regimes,
we define a simple yet effective source-free adaptation objective by utilizing
a novel instance-level weighting mechanism, named as Source Similarity Metric
(SSM). A thorough evaluation shows the practical usability of the proposed
learning framework with superior DA performance even over state-of-the-art
source-dependent approaches.

该研究提出了一种新的两阶段学习过程，以实现无监督域适应并实现源自由部署。在采购阶段，通过利用现有的源数据，在新颖的生成分类器框架中增强了模型拒绝源外分布样本的能力；在部署阶段，通过使用命名为源相似度度量的新型实例级加权机制，提出了一个简单而有效的源自由自适应目标，以设计一个统一的自适应算法，该算法可在各种范畴差异下运作，并实现了卓越的域适应性能。